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Analysing stillbirth data using dynamic self organizing maps

Matharage, S, Alahakoon, O, Alahakoon, D, Kapurubandara, S, Nayyar, R, Mukherjee, M, Jagadish, U, Yim, S and Alahakoon, I 2011, Analysing stillbirth data using dynamic self organizing maps, in Proceedings of the 22nd International Workshop on Database and Expert Systems Applications; DEXA 2011, IEEE, Piscataway, NJ, pp. 86-90, doi: 10.1109/DEXA.2011.14.

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Title Analysing stillbirth data using dynamic self organizing maps
Author(s) Matharage, S
Alahakoon, O
Alahakoon, D
Kapurubandara, S
Nayyar, R
Mukherjee, M
Jagadish, U
Yim, S
Alahakoon, I
Conference name International Workshop on Database and Expert Systems Applications (22nd : 2011 : Toulouse, France)
Conference location Toulouse, France
Conference dates 29 Aug. - 2 Sept. 2011
Title of proceedings Proceedings of the 22nd International Workshop on Database and Expert Systems Applications; DEXA 2011
Editor(s) [Unknown]
Publication date 2011
Conference series International Workshop on Database and Expert Systems Applications
Start page 86
End page 90
Total pages 5
Publisher IEEE
Place of publication Piscataway, NJ
Keyword(s) Stillbirths
Growing Self Organising Map
Clustering
Perinatal Mortality
Data Mining
GSOM
Summary Even with the presence of modern obstetric care, stillbirth rate seems to stay stagnant or has even risen slightly in countries such as England and has become a significant public health concern [1]. In the light of current medical research, maternal risk factors such as diabetes and hypertensive disease were identified as possible risk factors and are taken into consideration in antenatal care. However, medical practitioners and researchers suspect possible relationships between trends in maternal demographics, antenatal care and pregnancy information of current stillbirth in consideration [2]. Although medical data and knowledge is available appropriate computing techniques to analyze the data may lead to identification of high risk groups. In this paper we use an unsupervised clustering technique called Growing Self organizing Map (GSOM) to analyse the stillbirth data and present patterns which can be important to medical researchers.
ISBN 9781457709821
ISSN 1529-4188
Language eng
DOI 10.1109/DEXA.2011.14
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2011, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30060656

Document type: Conference Paper
Collection: Faculty of Business and Law
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Created: Tue, 18 Feb 2014, 13:42:26 EST by Sumith Matharage

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